Journal of Applied Probability · 2003 · 48 citations · 20 references
ℝ DEngineeringStochastic ProcessesTarget DensityMarkov KernelSampling TheoryMetropolis StepProbability TheoryMarkov Chain Monte CarloStochastic GeometryGeometric Ergodicity
In this paper, we consider the random-scan symmetric random walk Metropolis algorithm (RSM) on ℝ d . This algorithm performs a Metropolis step on just one coordinate at a time (as opposed to the full-dimensional symmetric random walk Metropolis algorithm, which proposes a transition on all coordinates at once). We present various sufficient conditions implying V -uniform ergodicity of the RSM when the target density decreases either subexponentially or exponentially in the tails.
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Monte Carlo Statistical Methods
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